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Zhen Li

Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.


Papers
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TL;DR: In insights into the activation of microglial autophagy by targeting surface ion channels to improve the treatment of PD and other neurodegenerative diseases, the athletic ability of PD mice treated by CS‐AT NPs and NIR‐II irradiation is significantly improved.
Abstract: Parkinson's disease (PD) is characterized with accumulation of Lewy bodies with a major component of fibrillar alpha‐synuclein (α‐syn). Herein, boosting PD therapeutic efficacy by enhancing the autophagy of microglia to phagocytose and degrade α‐syn via controlled opening of their surface TRPV1 channels with rationally designed photothermal nanoagent is reported. The Cu2−xSe‐anti‐TRPV1 nanoparticles (CS‐AT NPs) are fabricated to target the microglia and open their surface TRPV1 channels under the second near infrared (NIR‐II) laser irradiation to cause influx of Ca2+ to activate ATG5 and Ca2+/CaMKK2/AMPK/mTOR signaling pathway, which promote phagocytosis and degradation of α‐syn. The CS‐AT NPs are efficiently delivered by focused ultrasound into striatum of PD mice with high expression of TRPV1 receptors. The athletic ability of PD mice treated by CS‐AT NPs and NIR‐II irradiation is significantly improved due to the phagocytotic clearance of α‐syn by microglia with enhanced autophagy. The enzyme tyrosine hydroxylase, ionized calcium binding adapter protein 1, glial fibrillary acidic protein, and pSer129‐α‐syn (p‐α‐syn) of treated PD mice are almost recovered to the normal levels of healthy mice. This study provides insights into the activation of microglial autophagy by targeting surface ion channels to improve the treatment of PD and other neurodegenerative diseases.

35 citations

Journal ArticleDOI
TL;DR: In this paper, a surfactant-free emulsion RAFT polymerization of methyl methacrylate (MMA) without any hydrophilic macro-RAFT agent was successfully carried out in a continuous tubular flow reactor with a mixed solvent of water and dimethyl formamide (DMF).

35 citations

Journal ArticleDOI
TL;DR: Low-cost polycrystalline chalcopyrite films, which are successful as photovoltaic absorbers, are examined for application as PEC absorbers and it is demonstrated that CuGa3Se5 films are prime candidates for cheaply achieving efficient and durable PEC water splitting.
Abstract: Photoelectrochemical (PEC) water splitting is an elegant method of converting sunlight and water into H2 fuel. To be commercially advantageous, PEC devices must become cheaper, more efficient, and much more durable. This work examines low-cost polycrystalline chalcopyrite films, which are successful as photovoltaic absorbers, for application as PEC absorbers. In particular, Cu–Ga–Se films with wide band gaps can be employed as top cell photocathodes in tandem devices as a realistic route to high efficiencies. In this report, we demonstrate that decreasing Cu/Ga composition from 0.66 to 0.31 in Cu–Ga–Se films increased the band gap from 1.67 to 1.86 eV and decreased saturated photocurrent density from 18 to 8 mA/cm2 as measured by chopped-light current–voltage (CLIV) measurements in a 0.5 M sulfuric acid electrolyte. Buffer and catalyst surface treatments were not applied to the Cu–Ga–Se films, and they exhibited promising stability, evidenced by unchanged CLIV after 9 months of storage in air. Finally, fi...

34 citations

Journal ArticleDOI
TL;DR: The results suggest that NLK induces apoptosis in glioma cells via activation of caspases and may be a useful independent prognostic indicator for gliomas.

34 citations


Cited by
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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
15 Jul 2021-Nature
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

10,601 citations